In general, transport containers can be used to transport objects, e.g. goods in the field of production or sales. In this case, it may be necessary to recognize whether something, and if appropriate what, is situated in the transport container, e.g. when registering goods at a checkout exit (this may also be referred to as “Bottom of Basket” recognition—BoB). It is thus possible to reduce costs which arise if unregistered goods pass through the checkout exit without being recognized (this may also be referred to as loss prevention).
Computer-aided methods of pattern recognition are conventionally used to identify objects arranged in the transport container. In this case, distinctive patterns of the objects are recognized and compared with a database in which the patterns of known objects, e.g. goods, are stored.
The comparison of the patterns of objects contained in the transport container with the database and the capacity of the database may require a considerable outlay, particularly if a large number of objects are stored in the database with their respective patterns and/or if objects similar to one another are intended to be recognized. By way of example, it may be necessary to store for each object in each case a data set in accordance with various views of the object, if, for example, it cannot be ensured that the object is always present in the same orientation. Objects that are similar to one another may require a larger number of features to be compared in order to enable a reliable identification.
As the capacity of the database increases, that is to say as the number of data sets increases and/or the number of entries per data set increases, the storage requirement needed and the required computing power or required data analysis speed may increase in order to ensure temporally effective recognition of the objects, e.g. in real time. Furthermore, as the capacity of the database increases, the outlay for maintaining the data sets (data maintenance requirement) and hence the required personnel may increase in order to prevent an uncontrolled growth in the capacity of the database owing to obsolete data sets. Therefore, these systems for computer-aided pattern recognition may engender a considerable cost expenditure in terms of procurement and/or in terms of maintenance, particularly if e.g. a respective system is required per checkout exit. Moreover, the pattern recognition process is very complex in the case of a very high number of objects to be differentiated and the susceptibility to errors in the pattern recognition likewise increases as the number of patterns that are similar to one another increases.
Furthermore, a conventional recognition of the content of a transport container may be limited and/or inaccurate, e.g. in the case of flat objects (which have a small cross section e.g. in one orientation); objects having little or no accentuation and/or colouration, e.g. having only low information, colour and/or texture content (for example a virtually homogenous layout such as black, white, grey, etc.); and/or transparent objects. Furthermore, objects not stored in the database are regularly not recognized or recognized incorrectly.